def example():
"""simple test and performance measure
"""
num_user, num_item, ratings = load_ml_1m()
# suffle_data
np.random.shuffle(ratings)
# split data to training & validation
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# params
num_feature = 10
bmf_model = BayesianMatrixFactorization(num_user,
num_item,
num_feature,
train,
validation,
max_rating=5,
min_rating=1)
start_time = time.clock()
bmf_model.estimate(5)
end_time = time.clock()
print "time spend = %.3f" % (end_time - start_time)
return bmf_model
def example():
"""simple test and performance measure
"""
num_user, num_item, ratings = load_ml_1m()
# suffle_data
np.random.shuffle(ratings)
# split data to training & validation
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# params
num_feature = 10
mf_model = MatrixFactorization(
num_user, num_item, num_feature, train, validation, max_rating=5, min_rating=1, batch_size=100000
)
start_time = time.clock()
mf_model.estimate(5)
end_time = time.clock()
print "time spend = %.3f" % (end_time - start_time)
return mf_model
def setUp(self):
num_user, num_item, ratings = load_ml_1m()
np.random.shuffle(ratings)
self.num_user = num_user
self.num_item = num_item
self.ratings = ratings
self.num_feature = 10
train_pct = 0.9
train_size = int(train_pct * len(self.ratings))
self.train = self.ratings[:train_size]
self.validation = self.ratings[train_size:]
import numpy as np
from load_data import load_ml_1m, load_rating_matrix
from matrix_factorization import MatrixFactorization
from evaluation_metrics import RMSE
#from mf.bayesian_matrix_factorization import BayesianMatrixFactorization
if __name__ == "__main__":
# load MovieLens data
num_user, num_item, ratings = load_ml_1m()
np.random.shuffle(ratings)
# set feature numbers
num_feature = 10
# set max_iterations
max_iter = 20
# split data to training & testing
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# models
rec = MatrixFactorization(num_user, num_item, num_feature, train, validation, max_rating=5, min_rating=1)
# fitting
rec.estimate(max_iter)
